{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ebd66700",
   "metadata": {},
   "source": [
    "## Demo_TAC\n",
    "This is a demo for visualizing the Total Activation Change (TAC) of a Neuron Network\n",
    "\n",
    "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b950f4fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "! python ../../attack/badnet.py --save_folder_name badnet_demo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f81f973",
   "metadata": {},
   "source": [
    "or run the following command in your terminal\n",
    "\n",
    "```python attack/badnet.py --save_folder_name badnet_demo```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87bd9f5a",
   "metadata": {},
   "source": [
    "### Step 1: Import modules and set arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "71b7087b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os\n",
    "import yaml\n",
    "import torch\n",
    "import numpy as np\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib\n",
    "from matplotlib.patches import Rectangle, Patch\n",
    "\n",
    "sys.path.append(\"../\")\n",
    "sys.path.append(\"../../\")\n",
    "sys.path.append(os.getcwd())\n",
    "from visual_utils import *\n",
    "from utils.aggregate_block.dataset_and_transform_generate import (\n",
    "    get_transform,\n",
    "    get_dataset_denormalization,\n",
    ")\n",
    "from utils.aggregate_block.fix_random import fix_random\n",
    "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n",
    "from utils.save_load_attack import load_attack_result\n",
    "from utils.defense_utils.dbd.model.utils import (\n",
    "    get_network_dbd,\n",
    "    load_state,\n",
    "    get_criterion,\n",
    "    get_optimizer,\n",
    "    get_scheduler,\n",
    ")\n",
    "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2fb719c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Basic setting: args\n",
    "args = get_args(True)\n",
    "\n",
    "########## For Demo Only ##########\n",
    "args.yaml_path = \"../../\"+args.yaml_path\n",
    "args.result_file_attack = \"badnet_demo\"\n",
    "######## End For Demo Only ##########\n",
    "\n",
    "with open(args.yaml_path, \"r\") as stream:\n",
    "    config = yaml.safe_load(stream)\n",
    "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n",
    "args.__dict__ = config\n",
    "args = preprocess_args(args)\n",
    "fix_random(int(args.random_seed))\n",
    "\n",
    "save_path_attack = \"../..//record/\" + args.result_file_attack\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f959b510",
   "metadata": {},
   "source": [
    "### Step 2: Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8b67ac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "loading...\n",
      "max_num_samples is given, use sample number limit now.\n",
      "subset bd dataset with length:  5000\n",
      "Create visualization dataset with \n",
      " \t Dataset: bd_train \n",
      " \t Number of samples: 5000  \n",
      " \t Selected classes: [0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "# Load result\n",
    "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n",
    "selected_classes = np.arange(args.num_classes)\n",
    "\n",
    "# Select classes to visualize\n",
    "if args.num_classes>args.c_sub:\n",
    "    selected_classes = np.delete(selected_classes, args.target_class)\n",
    "    selected_classes = np.random.choice(selected_classes, args.c_sub-1, replace=False)\n",
    "    selected_classes = np.append(selected_classes, args.target_class)\n",
    "\n",
    "# keep the same transforms for train and test dataset for better visualization\n",
    "result_attack[\"clean_train\"].wrap_img_transform = result_attack[\"clean_test\"].wrap_img_transform \n",
    "result_attack[\"bd_train\"].wrap_img_transform = result_attack[\"bd_test\"].wrap_img_transform \n",
    " \n",
    "args.visual_dataset = 'bd_train'\n",
    "# Create dataset. Only support BD_TEST and BD_TRAIN\n",
    "if args.visual_dataset == 'bd_train':  \n",
    "    bd_train_with_trans = result_attack[\"bd_train\"]\n",
    "    visual_dataset = generate_bd_dataset(bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub, bd_only = True)\n",
    "elif args.visual_dataset == 'bd_test':\n",
    "    bd_test_with_trans = result_attack[\"bd_test\"]\n",
    "    visual_dataset = generate_bd_dataset(bd_test_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub, bd_only = True)\n",
    "else:\n",
    "    assert False, \"Illegal vis_class\"\n",
    "\n",
    "print(f'Create visualization dataset with \\n \\t Dataset: {args.visual_dataset} \\n \\t Number of samples: {len(visual_dataset)}  \\n \\t Selected classes: {selected_classes}')\n",
    "\n",
    "# Create data loader\n",
    "data_loader = torch.utils.data.DataLoader(\n",
    "    visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False\n",
    ")\n",
    "\n",
    "# Create denormalization function\n",
    "for trans_t in data_loader.dataset.wrap_img_transform.transforms:\n",
    "    if isinstance(trans_t, transforms.Normalize):\n",
    "        denormalizer = get_dataset_denormalization(trans_t)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f652e5",
   "metadata": {},
   "source": [
    "### Step 3: Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ff67e7b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load model preactresnet18 from badnet_demo\n"
     ]
    }
   ],
   "source": [
    "# Load model\n",
    "model_visual = generate_cls_model(args.model, args.num_classes)\n",
    "model_visual.load_state_dict(result_attack[\"model\"])\n",
    "model_visual.to(args.device)\n",
    "# !!! Important to set eval mode !!!\n",
    "model_visual.eval()\n",
    "print(f\"Load model {args.model} from {args.result_file_attack}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc952077",
   "metadata": {},
   "source": [
    "### Step 4: Collect Clean Features and BD Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "94612903",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting BD features from module Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Clean features from module Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting BD features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Clean features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting BD features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Clean features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting BD features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Clean features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting BD features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Clean features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting BD features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Clean features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting BD features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Clean features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting BD features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Clean features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting BD features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Clean features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting BD features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Clean features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting BD features from module Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "Collecting Clean features from module Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "Collecting BD features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Clean features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting BD features from module Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Clean features from module Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting BD features from module Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "Collecting Clean features from module Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "Collecting BD features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Clean features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting BD features from module Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Clean features from module Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting BD features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Clean features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting BD features from module Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Clean features from module Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting BD features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Clean features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting BD features from module Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "Collecting Clean features from module Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "Collecting BD features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Clean features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting BD features from module Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Clean features from module Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting BD features from module Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "Collecting Clean features from module Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "Collecting BD features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Clean features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting BD features from module Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Clean features from module Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting BD features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Clean features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting BD features from module Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Clean features from module Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting BD features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Clean features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting BD features from module Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "Collecting Clean features from module Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "Collecting BD features from module BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Clean features from module BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting BD features from module Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Clean features from module Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting BD features from module Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "Collecting Clean features from module Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "Collecting BD features from module BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Clean features from module BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting BD features from module Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Clean features from module Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting BD features from module BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Clean features from module BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting BD features from module Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Clean features from module Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting BD features from module Linear(in_features=512, out_features=10, bias=True)\n",
      "Collecting Clean features from module Linear(in_features=512, out_features=10, bias=True)\n"
     ]
    }
   ],
   "source": [
    "##### Plot for Attack #####\n",
    "\n",
    "module_dict = dict(model_visual.named_modules())\n",
    "module_names = module_dict.keys()\n",
    "\n",
    "# Plot Conv2d or Linear\n",
    "module_visual = [i for i in module_dict.keys() if isinstance(\n",
    "    module_dict[i], torch.nn.Conv2d) or isinstance(module_dict[i], torch.nn.Linear) or isinstance(module_dict[i], torch.nn.BatchNorm2d)]\n",
    "\n",
    "df = None\n",
    "\n",
    "max_num_neuron = 0\n",
    "for module_name in module_visual:\n",
    "    target_layer = module_dict[module_name]\n",
    "\n",
    "    print(f'Collecting BD features from module {target_layer}')\n",
    "    features_bd, labels_bd, *other_info = get_features(\n",
    "        args, model_visual, target_layer, data_loader, reduction='none', activation= None)\n",
    "\n",
    "    print(f'Collecting Clean features from module {target_layer}')\n",
    "    with temporary_all_clean(visual_dataset):\n",
    "        features_bd_clean, labels_clean, *other_info = get_features(\n",
    "            args, model_visual, target_layer, data_loader, reduction='none', activation= None)\n",
    "    \n",
    "    total_neuron = features_bd.shape[1]\n",
    "    max_num_neuron = np.max([max_num_neuron, total_neuron])\n",
    "    feature_diff = features_bd - features_bd_clean\n",
    "    np.abs(feature_diff, out = feature_diff) # inplace abs for faster computation\n",
    "    feature_abs_diff = feature_diff\n",
    "    # average over batch\n",
    "    feature_abs_diff_mean = np.mean(feature_abs_diff, axis=0)    \n",
    "    # average for each channel\n",
    "    if feature_abs_diff_mean.ndim >1:    \n",
    "        feature_abs_diff_mean = np.sum(feature_abs_diff_mean.reshape(total_neuron, -1), axis=1)\n",
    "        \n",
    "\n",
    "    for neuron_i in range(total_neuron):\n",
    "        base_row = {}\n",
    "        base_row['layer'] = module_name\n",
    "        base_row['Neuron'] = neuron_i\n",
    "        base_row['TAC'] = feature_abs_diff_mean[neuron_i]\n",
    "        if df is None:\n",
    "            df = pd.DataFrame.from_dict([base_row])\n",
    "        else:\n",
    "            df.loc[df.shape[0]] = base_row\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d82a565c",
   "metadata": {},
   "source": [
    "### Step 4: Show the TAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f65bfe12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ploting conv1\n",
      "ploting layer1.0.bn1\n",
      "ploting layer1.0.conv1\n",
      "ploting layer1.0.bn2\n",
      "ploting layer1.0.conv2\n",
      "ploting layer1.1.bn1\n",
      "ploting layer1.1.conv1\n",
      "ploting layer1.1.bn2\n",
      "ploting layer1.1.conv2\n",
      "ploting layer2.0.bn1\n",
      "ploting layer2.0.conv1\n",
      "ploting layer2.0.bn2\n",
      "ploting layer2.0.conv2\n",
      "ploting layer2.0.shortcut.0\n",
      "ploting layer2.1.bn1\n",
      "ploting layer2.1.conv1\n",
      "ploting layer2.1.bn2\n",
      "ploting layer2.1.conv2\n",
      "ploting layer3.0.bn1\n",
      "ploting layer3.0.conv1\n",
      "ploting layer3.0.bn2\n",
      "ploting layer3.0.conv2\n",
      "ploting layer3.0.shortcut.0\n",
      "ploting layer3.1.bn1\n",
      "ploting layer3.1.conv1\n",
      "ploting layer3.1.bn2\n",
      "ploting layer3.1.conv2\n",
      "ploting layer4.0.bn1\n",
      "ploting layer4.0.conv1\n",
      "ploting layer4.0.bn2\n",
      "ploting layer4.0.conv2\n",
      "ploting layer4.0.shortcut.0\n",
      "ploting layer4.1.bn1\n",
      "ploting layer4.1.conv1\n",
      "ploting layer4.1.bn2\n",
      "ploting layer4.1.conv2\n",
      "ploting linear\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fb2150d3700>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2664x3672 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_x0 = 0\n",
    "height = 1\n",
    "width = 1\n",
    "vmin = 0\n",
    "if args.normalize_by_layer:\n",
    "    vmax = 1\n",
    "else:\n",
    "    vmax = df.TAC.max()\n",
    "max_num_neuron = df.Neuron.max()\n",
    "\n",
    "norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax, clip=True)\n",
    "mapper = matplotlib.cm.ScalarMappable(norm=norm, cmap=matplotlib.cm.Blues)\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    figsize=(int(len(module_visual)), int(max_num_neuron/10)))\n",
    "\n",
    "ax.plot([0, 0], [0, 0])\n",
    "\n",
    "for module_name in module_visual:\n",
    "    print(f'ploting {module_name}')\n",
    "    y_0 = 0\n",
    "    layer_info = df[df.layer == module_name]\n",
    "    layer_tac_max = layer_info['TAC'].max()\n",
    "    total_neuron = layer_info.shape[0]\n",
    "    for neuron_i in range(total_neuron):\n",
    "        x_0 = start_x0\n",
    "        base_row = layer_info.iloc[neuron_i]\n",
    "        if args.normalize_by_layer:\n",
    "            ax.add_patch(Rectangle((x_0, y_0), width, height,\n",
    "                facecolor=mapper.to_rgba(base_row['TAC']/layer_tac_max),\n",
    "                fill=True,\n",
    "                lw=5,\n",
    "                alpha=0.8))\n",
    "\n",
    "        else:\n",
    "            ax.add_patch(Rectangle((x_0, y_0), width, height,\n",
    "                            facecolor=mapper.to_rgba(base_row['TAC']),\n",
    "                            fill=True,\n",
    "                            lw=5,\n",
    "                            alpha=0.8))\n",
    "\n",
    "        y_0 += 1.5*height\n",
    "    start_x0 += 1.5*width\n",
    "x_loc = [0.5*width+1.5*width*i for i in range(len(module_visual))]\n",
    "y_loc = [0.5*height+1.5*height*i for i in range(max_num_neuron)]\n",
    "\n",
    "ax.set_xlim(xmin=-0.5*width, xmax=1.5*width*(len(module_visual)+1))\n",
    "ax.set_ylim(ymin=-0.5*height, ymax=1.5*height*(max_num_neuron+1))\n",
    "ax.set_xticks(x_loc, module_visual, rotation=270)\n",
    "ax.set_yticks(y_loc[::10], np.arange(max_num_neuron)[::10])\n",
    "ax.set_title(f'TAC of {len(visual_dataset)} Images')\n",
    "ax.set_ylabel('Neuron')\n",
    "ax.set_xlabel('Layer')\n",
    "\n",
    "cb_ax = fig.add_axes([0.15, 0.9, 0.7, 0.01])\n",
    "\n",
    "fig.colorbar(mapper,\n",
    "             cax=cb_ax, orientation=\"horizontal\", label='TAC')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('py38')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13 (default, Oct 21 2022, 23:50:54) \n[GCC 11.2.0]"
  },
  "vscode": {
   "interpreter": {
    "hash": "6869619afde5ccaa692f7f4d174735a0f86b1f7ceee086952855511b0b6edec0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
